BLE localization using RSSI measurements and iRingLA

Thaljaoui, Adel; Val, Thierry; Nasri, Nejah; Brulin, Damien

doi:10.1109/icit.2015.7125418

Cited by 81 publications

(30 citation statements)

References 6 publications

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“…Current localization algorithms for BLE beacons can be divided into three classes: proximity [ 21 , 22 , 23 ], range-based [ 21 , 24 , 25 , 26 ] and FP [ 4 , 21 , 27 , 28 ]. In the first class, the work [ 21 ] evaluates the performance of BLE localization by using proximity in a multi-floor building.…”

Section: Related Workmentioning

confidence: 99%

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Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Zhuang

Yang

et al. 2016

Sensors

393

230

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Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.

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Section: Related Workmentioning

confidence: 99%

“…In the second class, a PM is used in a multi-floor building for BLE localization [ 21 ]. The research [ 24 ] presents a modified PM, called iRingLA, for BLE localization, in which the ring is used instead of circle for trilateration. Another approach called stigmergic is presented for range-based indoor localization [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Zhuang

Yang

et al. 2016

Sensors

393

230

View full text Add to dashboard Cite

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“…Given at least three anchored devices, if distance to these anchored devices can be inferred from new RSS measurements, the location of the target can be easily calculated using simple geometry techniques such as hyperbolic and circular approaches. The hyperbolic approach converts the geometric problem in linear form and solves it through a closed form of the least square method [13,14]. On the other hand, the circular approach iteratively solves the optimization problem [15,16] through a gradient step.…”

Section: Related Workmentioning

confidence: 99%

Study of Activity Tracking through Bluetooth Low Energy-Based Network

Mohsin

Payandeh

Ho³

et al. 2019

Journal of Sensors

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This paper proposes a proof-of-concept, low-cost, and easily deployable Bluetooth low energy- (BLE-) based localization system which actively scans and localizes BLE beacons attached to mobile subjects in a room. Using the received signal strength (RSS) of a BLE signal and the uniqueness of BLE hardware addresses, mobile subjects can be identified and localized within the hospital room. The RSS measurement of the BLE signal from a wearable BLE beacon varies with distance to the wall-anchored BLE scanner. In order to understand and demonstrate the practicality of the relationship between RSS of a BLE beacon and the distance of a beacon from a scanner, the first part of the paper presents the analysis of the experiments conducted in a low-noise and nonreflective environment. Based on the analysis conducted in an ideal environment, the second half of the paper proposes a data-driven localization process for pinpointing the movements of the subject within the experimental room. In order to ensure higher accuracy like fingerprinting techniques and handle the increased number of BLE-anchored scanners like geometric techniques, the proposed algorithm was designed to combine the best aspects of these two techniques for better localization. The paper evaluates the effects of the number of BLE wall-mounted scanners and the number of packets on the performance of the proposed algorithm. The proposed algorithm locates the patient within the room with error less than 1.8 m. It also performs better than other classical approaches used in localization.

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“…그리고 Blutetooth는 실내 측위를 위해 설치 및 유 지 면에서 경제성은 우수하나 통신 거리가 짧다는 단 점으로 인해 측위 정밀성을 보장하지 못한다 [3] . 의 삼변 측량을 수행하는 방식이다 [8] . 수 있다 [7] .…”

Section: 활용되었다 하지만 최근에 들어 실내 위치 인식 기술unclassified

Precise Indoor Positioning Algorithm for Energy Efficiency Based on BLE Fingerprinting

Lee¹,

Lee²

2016

The Journal of Korean Institute of Communications and Informati

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As Indoor Positioning System demands due to increased penetration and utilization of smart device, Indoor Positioning System using Wi-Fi or BLE(Bluetooth Low Energy) beacon takes center stage. In this paper, a terminal location of the user is calculated through Microscopic Trilateration using RSSI based on BLE. In the next step, a fingerprinting map appling approximate value of Microscopic Trilateration increases an efficiency of computation amount and energy for Indoor Positioning System. I suggest Indoor Positioning Algorithm based on BLE fingerprinting considering efficiency of energy by conducting precise Trilateration that assure user's terminal position by using AP(Access Point) surrounding targeted fingerprinting cells. And This paper shows experiment and result based on An Suggesting Algorithm in comparison with a fingerprinting based on BLE and Wi-Fi that be used for Indoor Positioning System.

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BLE localization using RSSI measurements and iRingLA

Cited by 81 publications

References 6 publications

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Study of Activity Tracking through Bluetooth Low Energy-Based Network

Precise Indoor Positioning Algorithm for Energy Efficiency Based on BLE Fingerprinting

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